OLS Regression Results
| Dep. Variable: | finishers | R-squared: | 0.061 |
| Model: | OLS | Adj. R-squared: | 0.049 |
| Method: | Least Squares | F-statistic: | 5.083 |
| Date: | Tue, 01 Nov 2022 | Prob (F-statistic): | 0.0270 |
| Time: | 16:08:37 | Log-Likelihood: | -308.42 |
| No. Observations: | 80 | AIC: | 620.8 |
| Df Residuals: | 78 | BIC: | 625.6 |
| Df Model: | 1 | | |
| Covariance Type: | nonrobust | | |
| coef | std err | t | P>|t| | [0.025 | 0.975] |
| Intercept | 38.8180 | 2.613 | 14.855 | 0.000 | 33.616 | 44.020 |
| lauf | -0.1264 | 0.056 | -2.255 | 0.027 | -0.238 | -0.015 |
| Omnibus: | 10.204 | Durbin-Watson: | 1.899 |
| Prob(Omnibus): | 0.006 | Jarque-Bera (JB): | 10.207 |
| Skew: | 0.839 | Prob(JB): | 0.00607 |
| Kurtosis: | 3.494 | Cond. No. | 94.2 |
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.